MOBILE DEVICE ENHANCED SHOPPING EXPERIENCE

• US 20100042469A1
• Filed: 08/18/2008
• Published: 02/18/2010
• Est. Priority Date: 08/18/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Tsang, Michael, Chang, Eric I., Chandrasekar, Raman, Bai, Tian
• Application Number:
  US12/193,677
• Publication Number:
  US 20100042469A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0267 Wireless devices
• CPC Class Codes: G06Q 30/0268 at point-of-sale [POS]
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]

Abstract

A software and/or hardware facility for providing a mobile device enhanced shopping experience is disclosed. The facility may determine a user characteristic of a mobile device user and deliver information in response to a product query. The information may include product information, competitive pricing information, competitive product information, related product information, a product advertisement, and/or the like. Also, the product query may be based on barcode information, a barcode image, product information, a product image, and/or the like.

Claims

1. A method for enhancing a shopping experience, comprising:

determining a user characteristic based on a user interaction context including information on a user interaction with a first shopping information message that was previously delivered to a mobile device;

receiving an indication of a product query; and

selectively delivering a second shopping information message to the mobile device based on the determined user characteristic and the received indication.

2. The method of claim 1, wherein the second shopping information message includes at least one of product information, competitive pricing information, competitive product information, related product information, or a product advertisement.

3. The method of claim 1, wherein the second shopping information message includes a coupon.

4. The method of claim 1, wherein the indication includes at least one of barcode information, a barcode image, product information, or a product image.

5. The method of claim 1, wherein the user characteristic is further based on a user context including at least one of natural language information based on a user interaction with the mobile device or sentiment information based on the user interaction with the mobile device.

6. The method of claim 5, wherein determining the user characteristic includes:

extracting the natural language information from the user interaction with the mobile device; and

extracting the sentiment information from the user interaction with the mobile device.

7. The method of claim 5, wherein determining the user characteristic includes:

receiving user speech data; and

extracting at least one of the natural language information or the sentiment information from the received user speech data.

8. The method of claim 1, further comprising:

determining a mobile device characteristic based on a mobile device context of a mobile device, wherein selectively delivering the second shopping information message is further based on the mobile device context.

9. The method of claim 8, wherein the mobile device context includes a location of the mobile device.

10. The method of claim 1, wherein selectively delivering the second shopping information message includes:

selecting an advertisement from multiple advertisements based on the determined user characteristic, the received indication, and an advertisement effectiveness characteristic that includes at least one of a redemption rate, response rate, discount amount, community-based feedback, advertiser popularity, product popularity, or service popularity of a selectively delivered advertisement.

11. A processor-readable medium containing instructions for executing a method of enhancing a shopping experience, wherein the method comprises:

determining a user characteristic based on a user interaction context including information on a user interaction with a first shopping information message that was previously delivered to a mobile device;

determining a mobile device characteristic based on a mobile device context of a mobile device;

receiving an indication of a product query including a barcode image;

selecting an advertisement from multiple advertisements based on the determined user characteristic, the mobile device context, the received indication, and an advertisement effectiveness characteristic that includes a redemption rate, response rate, and discount amount of a selectively delivered advertisement; and

selectively delivering the selected advertisement in a second shopping information message to the mobile device based on the determined user characteristic, the mobile device context, and the received indication.

12. The processor-readable medium of claim 11, wherein the second shopping information message includes at least one of product information, competitive pricing information, competitive product information, related product information, or a product advertisement, and wherein the indication further includes at least one of product information or a product image.

13. The processor-readable medium of claim 11, wherein the user characteristic is further based on a user context including at least one of natural language information based on a user interaction with the mobile device and sentiment information based on the user interaction with the mobile device.

14. The processor-readable medium of claim 11, wherein the mobile device context includes a location of the mobile device.

15. The method of claim 11, wherein determining the user characteristic includes:

receiving user speech data; and

extracting at least one of natural language information or sentiment information from the received user speech data.

16. The processor-readable medium of claim 11, wherein the advertisement effectiveness characteristic further includes at least one of community-based feedback, advertiser popularity, product popularity, or service popularity of the selectively delivered advertisement.

17. A computing system configured to enhance a shopping experience, comprising:

a memory;

a first module configured, when executed in the memory, to determine a user characteristic based on a user interaction context including information on a user interaction with a first shopping information message that was previously delivered to a mobile device;

a second module configured, when executed in the memory, to receive an indication of a product query; and

a third module configured, when executed in the memory, to selectively deliver a second shopping information message to the mobile device based on the determined user characteristic and the received indication.

18. The computing system of claim 17, wherein the second shopping information message includes at least one of product information, competitive pricing information, competitive product information, related product information, or a product advertisement, and wherein the indication includes at least one of barcode information, a barcode image, product information, or a product image.

19. The computing system of claim 17, further comprising:

a fourth module configured, when executed in the memory, to determine a mobile device characteristic based on a mobile device context including a location of a mobile device, wherein selectively delivering the second shopping information message is further based on the mobile device context.

20. The computing system of claim 17, wherein selectively delivering the second shopping information message includes:

selecting an advertisement from multiple advertisements based on the determined user characteristic, the received indication, and an advertisement effectiveness characteristic that includes at least one of a redemption rate, response rate, discount amount, community-based feedback, advertiser popularity, product popularity, or service popularity of a selectively delivered advertisement.